Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

The commodity market has moved swiftly from a marketplace linking suppliers and end-users to a market which also includes a full range of investors who are speculating, hedging and taking complex positions.

What are the statistical consequences?

Commodity producers must choose investments based on long run measures of risk and reward.

In this paper I will try to assess the long run risk in these markets.

Using daily data from 1996 to July, 2012, annualized measures of means and volatilities are constructed for 21 different commodities. These are roughly divided into agricultural, industrial, precious metals and energy products.

What annual return from today will be worse than the actual return 99 out of 100 times?

What is the 1% quantile for the annual percentage change in the price of an asset?

Assuming constant volatility and a normal distribution, it just depends upon the volatility. Here is the result. Here also is the actual 1% quantile of overlapping annual returns for each series since 1996.

This is a new method for estimating betas that are not constant over time and is particularly useful for financial data. See Engle(2012).

It has been used to determine the expected capital that a financial institution will need to raise if there is another financial crisis and here we will use this to estimate the fall in commodity prices if there is another global financial crisis.

It has also been used in Bali and Engle(2010,2012) to test the CAPM and ICAPM.

We require an estimate of the conditional covariance matrix and possibly the conditional means in order to express the betas.

In regressions such as one factor or multi-factor beta models or money manager style models or risk factor models, the means are insignificant and the covariances are important and can be easily estimated.

In one factor models this has been used since Bollerslev, Engle and Wooldridge(1988) as